Computational Systems Biology Lab

Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.

Blog article: It takes two to tango — making bio-production more economical (RBCDSAI Blog)

Original Paper: 

• L. Raajaraam and K. Raman, “A Computational Framework to Identify Metabolic Engineering Strategies for the Co-Production of Metabolites,” Frontiers in Bioengineering and Biotechnology, vol. 9, p. 1330, 2022.
  [bibtex]

  @article{Raajaraam2022Computational,
    title = {A {{Computational Framework}} to {{Identify Metabolic Engineering Strategies}} for the {{Co-Production}} of {{Metabolites}}},
    author = {Raajaraam, Lavanya and Raman, Karthik},
    year = {2022},
    journal = {Frontiers in Bioengineering and Biotechnology},
    volume = {9},
    pages = {1330},
    issn = {2296-4185},
    doi = {10.3389/fbioe.2021.779405},
    pmid = {35071202},
    abstract = {Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.},
  }